This September 30, 2025 paper detail research into **Brain Dynamics Hypothesis (BDH)** models, particularly the **BDH-GPU** architecture, which proposes a biologically-inspired alternative to the standard Transformer model for language processing and reasoning. The core idea is to create AI systems that generalize reasoning like humans by modeling intelligence as the **emergence of reasoning from neuron-to-neuron interactions**, rather than centralized computation. The research highlights the limitations of current Transformer architectures in systematically generalizing chain-of-thought reasoning over long sequences and suggests that BDH models, based on **local graph dynamics and Hebbian learning**, offer a more practical and efficient approach, especially for enterprise settings and long-context inference. The sources frame this work as a move towards **Axiomatic AI**, seeking a micro-foundational understanding of model behavior over time, and demonstrate through empirical findings that BDH-GPU exhibits desirable properties like a **scale-free network structure** and favorable scaling laws compared to GPT2-like models.Sources:https://arxiv.org/pdf/2509.26507https://www.forbes.com/sites/victordey/2025/10/08/can-ai-learn-and-evolve-like-a-brain-pathways-bold-research-thinks-so/

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